Doctoral Consortium II

Listening to Digital Fatigue: Neurodivergent Students’ Narratives of Embodiment, Belonging, and Online Learning

Giselle Tadman

Lancaster University

Abstract:

This study explores neurodivergent university students’ experiences – and makes sense – of digital fatigue in post-pandemic online learning environments. Using a qualitative, experience-centred narrative analysis design, it applies Voice-Centred Relational Method (VCRM) alongside Theory of Practice Architectures (TPA) to examine how cultural-discursive, material-economic, and social-political structures shape students’ embodied and relational experiences of fatigue. Findings show digital fatigue as cyclical, structurally produced, and closely tied to sensory load, institutional pacing, interface design, and relational isolation, while also highlighting the protective role of assistive technologies, flexible pacing, humour, and supportive tutor relationships. This study offers originality through narrative voices, embodiment, and structural analysis to illuminate digital fatigue as a systemic phenomenon instead of individual limitations. Insights generated have implications for higher education designers, digital learning developers, disability support teams, and educators seeking to create more accessible, relational, and neurodivergent-inclusive online learning environments.

Improving learners’ attention in synchronous online ESOL classes through the integration of visual aids: An action research and eye tracking study

Truong Do, Ursula Stickler, Lijing Shi, Irina Rets

The Open University, UK

Abstract:

Maintaining learner attention in synchronous online ESOL (English for Speakers of Other Languages) classes remains a persistent pedagogical challenge in CALL. Although visual aids are frequently incorporated into language teaching, most empirical research examining their impact has been conducted in face-to-face contexts. Existing findings are mixed, reporting both facilitative and distracting effects, and relatively few studies explore how visual aids shape sustained attention in live online ESOL classrooms. My research project investigates how the use of visual aids may facilitate adult ESOL learners’ attention during synchronous online lessons, drawing on an action research framework.

Situated within UK adult online ESOL provision, the study adopts an interpretivist paradigm and unfolds across three iterative action research cycles. In each cycle, teacher participants design, implement, and refine their own lessons incorporating visual aids, such as images and videos. Learners’ eye movements are captured during selected lesson segments using webcam-based eye tracking, an emerging and accessible methodological tool in CALL research. Quantitative eye-tracking data is triangulated with classroom observations and post-lesson learner interviews, which inform follow-up teacher discussions and action planning for subsequent cycles. My initial findings from the three cycles will also be presented.

AI-Mediated Student Support in Technology-Enhanced Learning: A Design-Based Research Study of Inclusive Engagement in Higher Education

Mahbub Rahman

Lancaster University

Abstract:

This doctoral research examines how an Ai-enabled student support application can be evaluated and iteratively redesigned to enhance engagement and academic participation for students with additional needs in higher education. While AI is increasingly embedded within institutional infrastructures, there remains limited empirical work examining how AI-mediated support operates within inclusive practices, particularly in Global South contexts.

The study adopts a design-based research (DBR) approach to investigate a newly launched AI-enabled support application implemented within a transnational university in Egypt. The research is structured across two iterative cycles of analysis, redesign, and evaluation. Cultural Historical Activity theory (CHAT) is used to examine how student engagement and access to support are mediated through the AI system within the institutional activity system, while Universal Design for Learning (UDL) informs the development of inclusive design modifications.

The first phase focuses on exploring how students with additional needs experience and engage with the AI-enabled application, alongside institutional factors shaping its use. The second phase will examine how iterative redesign may influence AI-mediated engagement, accessibility, and participation within existing support structures.

By situating the AI-mediated support system within a real institutional context, this study aims to contribute to ongoing discussions in technology enhanced learning (TEL) regarding inclusive digital provision and the role of AI in mediating support processes.

When Competence Fails Under Pressure: Designing AI-Supported Simulation for Emotional Intelligence and Decision-Making in Project Management

Kristen Karmazinuk

Lancaster University

Abstract:

Professional education often assumes that competence transfers directly to performance under pressure. However, failure in applied contexts frequently arises not from knowledge deficits, but from miscalibrated judgement under cognitive and emotional strain. While emotional intelligence (EI) is widely recognized as critical to professional practice, it remains inconsistently operationalized and is rarely embedded in technology-enhanced learning (TEL) environments to support decision-making under pressure.

This doctoral research, currently in its early conceptual and design stages, investigates how to design TEL systems to improve decision-making under pressure by supporting emotional regulation and decision calibration. In this research, decision-making under pressure is conceptualized as the alignment among judgement, confidence, and action under performance pressure, and reframes EI not as a static trait but as a trainable and measurable capability enacted in context.

Adopting a Design Science Research (DSR) approach, the research proposes developing an AI-supported simulation environment in which learners engage in time-constrained, socially interactive project scenarios. The proposed system integrates adaptive scaffolding into conversational, scenario-based simulations, where AI-driven stakeholders and team members respond dynamically to learners’ decisions. These interactions are intended to surface moments of miscalibration and support real-time reflection through prompts, confidence checks, and feedback.

A mixed-methods evaluation is planned to examine how such scaffolds may influence decision-making quality, confidence alignment, and reflective processes under pressure. At this stage, the research focuses on refining the conceptual model, defining system requirements, and informing the design of an initial prototype for subsequent empirical testing.

This research contributes to the design of TEL environments by embedding emotional and cognitive processes within AI-supported simulations for professional learning. It further addresses a critical gap between competence and performance under pressure, demonstrating how AI-supported environments can operationalize EI within decision-making contexts.

Feedback is sought on: (1) operationalizing calibration under stress; (2) capturing real-time emotional regulation in simulation-based environments; and (3) designing adaptive AI scaffolds that support reflection without reducing learner agency.

GenAI-Driven Assessment in Business Schools: Rethinking Trust, Pedagogy, and Outcomes

Peter Birdsall

The Open University, UK & Wittenborg University of Applied Sciences

Abstract:

The swift adoption of Generative Artificial Intelligence (GenAI) in higher education is transforming assessment methods, institutional practices, and the skills graduates develop. Although current research highlights the potential benefits and challenges of GenAI in education, there is limited empirical evidence on its broader influence on assessment systems, the changing psychological contract between students and institutions, and its effects on graduate employability, especially in business schools.

This study examines how GenAI impacts assessment and learning in higher education (business schools) using a framework that covers micro (students and faculty), meso (institutions), and macro (labour and policies) levels. It highlights the shift from traditional assessment to algorithmic evaluation and warns that, without careful redesign, GenAI could reinforce standardisation rather than promote human-centred learning.

The study uses a multi-phase mixed-methods approach rooted in critical realism and pragmatism. It includes qualitative interviews and focus groups with students, faculty, and employers to explore perceptions of fairness, trust, and GenAI in assessments. These insights inform longitudinal surveys tracking changes in GenAI literacy, assessment views, and employability confidence. A quasi-experimental intervention involving a GenAI and prompt-engineering module examines how structured GenAI interaction affects academic and employment outcomes. Also, document and policy analysis add institutional context.

This study advances both theory and practice in three key ways. First, it creates an integrated framework connecting GenAI adoption, assessment redesign, psychological contract dynamics, and employability outcomes. Second, it provides empirical insights into trust, transparency, and fairness in assessments mediated by GenAI. Third, it offers guidance for policy and strategic initiatives for business schools aiming to balance assessment innovation with ethical governance and labour market requirements.

The findings are intended to aid in creating human-centred, transparent, and employability-focused assessment systems in the growing context of GenAI-mediated higher education.

Emotional Profiles of Generative AI Adoption: A Mixed Methods Study of Distance Learning Educators in Higher Education

Kendal Wright

The Open University, UK

Abstract:

Emotional Profiles of Generative AI Adoption: A Mixed Methods Study of Distance Learning Educators in Higher Education

This EdD research project explores the emotional dimensions of generative AI (GenAI) adoption among distance learning educators in UK higher education. The study is guided by two aims: to explore the emotional dimensions of GenAI adoption, and to develop an adapted Technology Acceptance Model that incorporates emotional dimensions. While existing technology acceptance models consider cognitive aspects, emerging evidence suggests that emotions significantly shape a user’s engagement with rapidly evolving AI tools.

The project, at its current stage is considering a pragmatist paradigm. Enabling both methodological flexibility and a practical output focus. There will be two phases; phase one involves an online survey combining closed and open questions. The data will be analysed through hierarchical cluster analysis. Phase two will use purposive sampling to conduct semi structured interviews that explore how educators grouped within different emotional profiles work with GenAI within the distance learning higher education context. The two phase, sequential design ensures that the qualitative inquiry is grounded by empirically identified patterns.

Having the opportunity to present this research in its current stage at the CALRG Doctoral Consortium will provide a valuable opportunity to discuss a number of areas including the methodological and ethical challenges of researching emotions in technology adoption, and the grouping of educators, especially as a researcher embedded within the chosen research environment. I am seeking feedback on the development of the adapted technology acceptance model, the robustness of the cluster analysis approach for identifying emotional profiles, and strategies for maintaining reflexivity given my positionality.

This research aims to generate theoretically informed yet practically relevant insights that support educators navigating GenAI in distance learning contexts, aligning closely with CALRG’s commitment to advancing digital education research.

Doctoral Consortium I

Understanding Teacher Educators’ Reflective Practices: Early Findings From a Needs Analysis Informing AI–Supported Reflection

Mireille Pinas

The Open University, UK

Abstract:

Reflection is recognised as central to teachers’ professional development, supporting critical thinking, self-evaluation and informed pedagogical decision-making (Larrivee, 2008). Yet despite its importance, relatively little is known about the reflective skills and practices of university-based teacher educators (Logan et al., 2025). In practice, higher education faculty find it challenging to engage in meaningful reflection due to limited protected time and insufficient institutional support (Bray & Fotheringham, 2022).

A further challenge lies in the reflective practice literature. While established models offer valuable conceptual foundations, they are not designed for the distinctive, multifaceted nature of teacher educators’ work. Teacher educators navigate responsibilities including teaching, mentoring, curriculum design and researching their practice. This complexity increases the need for reflection to make informed decisions across overlapping roles. Yet existing frameworks do not fully account for this complexity or the need to navigate multiple roles simultaneously. Furthermore, most models focus on either stages of reflection or depth of reflective thinking, offering limited practical or role-sensitive scaffolding. This limitation may be particularly pronounced for teacher educators, given their varied roles.

In response to these issues, my doctoral study seeks to develop a reflective framework grounded in reflective theory and responsive to the multifaceted nature of teacher educators’ work. The study explores how generative AI (GenAI) can serve as a “critical friend,” guiding teacher educators by posing reflective questions and offering personalised, dialogic, and flexible support. Emerging work in GenAI suggests that these tools may prompt deeper thinking (Al-Fattal, 2025), yet their use for supporting teacher educators’ reflection remains underexplored.

This presentation reports early findings from the first phase of the study, a needs analysis based on semi-structured interviews with university-based teacher educators in the UK. It offers initial insights into their reflective practices across multiple roles and their perceptions of artificial intelligence for reflection.

Exploring Transformative Agency in AI-Mediated Foreign Language Learning: A Study of chatbot-mediated Language Learning using Activity Theory

Patricia Lanners-Kaminski

Lancaster University

Abstract:

While research on artificial intelligence in language education has grown rapidly, much of this work has focused on learning outcomes or technological affordances. Less attention has been paid to how learners actively engage with AI tools and how such engagement may reshape their learning practices over time. This doctoral study addresses that gap by examining active engagement with AI through the lens of Transformative Agency in AI-mediated foreign language learning, in chatbot-mediated environments. Transformative Agency is understood here as learners’ capacity to question, reinterpret, and deliberately reshape established ways of learning, making it a useful concept for analyzing active engagement with AI beyond mere tool use.

To understand how Transformative Agency develops, the study looks beyond isolated instances of chatbot use to the broader activity systems in which learning takes place. Drawing on Cultural-Historical Activity Theory (CHAT), it conceptualizes chatbot-mediated language learning as a dynamic activity system in which AI chatbots function as mediating artefacts embedded in broader social and institutional contexts. Particular attention is given to contradictions within and between elements of these activity systems and to how such tensions may drive change. Within this framework, Transformative Agency is used to examine how learners question, reinterpret, and potentially transform their learning practices in response to these tensions.

The study adopts a qualitative design based on semi-structured interviews with language learners who use AI chatbots as part of their language-learning activity systems. The project seeks to generate a contextualized understanding of how learners experience AI-mediated language learning and how they enact Transformative Agency in relation to chatbot use, including possible shifts in goals, strategies, roles, and responsibilities.

The study aims to contribute to research on AI in education by providing a systemic and developmental account of how Transformative Agency emerges in learners’ engagement with AI, rather than foregrounding technological functionality.

Re-Thinking Assessment in the Age of Artificial Intelligence

Denise G. Taylor

Lancaster University

Abstract:

This study used the interventionist methodology, CHAT-informed Change Laboratory with an expansive learning cycle to re-imagine what we are measuring and why we assess in the Age of AI. Throughout collaborative workshops with educators from diverse educational contexts, this study explored a central pedagogical challenge posed by the ubiquity of generative AI: if students can produce work that is indistinguishable from their own, what, and how, should we assess?

Beginning with a shared recognition that AI is a disruptor of traditional output-based assessment, workshop participants examined the distinction between AI as an assistant and AI as a substitute for student thinking. Early discussions raised tensions among institutional policy, equity concerns, and the validity of existing assessment frameworks, with participants questioning whether existing assessment criteria could still serve as reliable indicators of genuine learning.

From these dialogues, the group co-constructed a six-criterion rubric designed to shift the locus of assessment from product to process. The criteria: Reasoning Trail, Evidence Quality, AI Declaration and Prompt Transparency, Personal Situatedness, Idea First, Language Later, and Live Accountability, each require students to make their thinking visible at successive stages of their work, generating process artefacts that AI cannot easily replicate or simulate.

Subsequent workshops focused on trialling the rubric in participants’ own classrooms. Feedback revealed both promise and friction. The criteria most consistently valued were those requiring personal situatedness and visible ideation, while live accountability tasks proved a practical alternative to formal viva-style examinations. Persistent challenges included teacher readiness for process-based assessment, institutional policy constraints, and a minority view among participants that AI has already rendered any criterion-based assessment fundamentally unreliable.

Participants concluded by arguing that authentic assessment in a generative AI landscape requires a structural shift: from assessing what students produce, to evidencing how and why they think.

Learning Through Mediated Problem-Solving in Engineering Final Year Projects

Deepa Gundala Vijayaraghavan

Lancaster University

Abstract:

Final year projects (FYPs) are capstone experiences that prepare engineering students for professional practice, which is a measure of the transformation of knowledge, they acquire through their academic program. Whilst many studies have examined the administration and outcomes of FYPs, there remains limited analytical understanding of how learning unfolds progressively within these complex, tool-rich environments. This study investigates how students engage with multiple mediational resources during FYPs and how such engagements shape their learning trajectories and development of industry readiness attributes.

Drawing on an Activity Theory perspective, the study analyses 200 micro episodes of student activity across different phases of FYP work. The analysis focuses on how disturbances[contradictions] arising within the activity system—such as tool unfamiliarity, knowledge gaps, resource constraints, and team-based tensions—trigger mediated actions. Students mobilised a range of mediational artefacts, including simulation tools, online resources, peer collaboration, and AI-based tools, to navigate these challenges.

The findings indicated that learning was characterised by trajectories moving from disturbance to stabilisation through mediated action, viz: From Double Stimulation to Ascending from abstract to concrete [DS →ATC], Moving from abstract to concrete [ATC]. Students were able to reorganise their activity, leading to the development of practical competencies such as troubleshooting, adaptive problem solving and collaborative coordination. Some episodes resulted in a “critical learning impasse” [ IMPASSE], where constraints could not be effectively negotiated through available mediational means, limiting both activity progression and skill development. These can be understood as critical touchpoints where intervention may enhance learning outcomes in FYPs.

The study indicated that learning in FYPs is not linear or solely driven by prior knowledge but emerges through engagement with the evolving object of activity mediated by diverse tools and resources. It is also shaped by how students construe the object of the activity. By illustrating how learning is enabled or constrained, the findings can be used as directives for designing learning environments and re-design curriculum that better support students in navigating complex, real-world engineering tasks and bridge the gap between curriculum and industry requirements.

Rethinking Programming Education through Physical Computing: A Case Study approach on Level 1 Computing and Communications at the Open University

David McDade

The Open University, UK

Abstract:

Symposium: Reimagining Research Skills Development for UK Social Scientists: co-creating the Research Capability Hub

Bart Rienties, Duygu Bektik, Carina Bossu, Elizabeth Fitzgerald

Institute of Educational Technology (IET), The Open University, UK.

Abstract:

How should a national research training system be designed when existing provision is fragmented across institutions, sectors and career stages? The Research Capability Hub (RCH: rch.ac.uk) funded by The Economic and Social Research Council (ESRC) and led by The Open University, is building a federated infrastructure to address this question. Rather than creating courses in isolation, the RCH aggregates, quality-assures and connects training resources from universities, government and the third sector across all four UK nations, delivering them through a dual-platform architecture combining a discovery hub with OpenLearn Create for structured learning.

This symposium presents early insights from the Hub’s first operational year across five interconnected workstreams. The first presentation frames the RCH within ESRC strategy, positioning research capability as national infrastructure. The second reports on ecosystem curation: 934 resources catalogued across 15 sources and 12 thematic areas, revealing significant gaps in existing provision. The third examines co-creation, drawing on Q-methodology data, evidence cafes, and a set of ten co-creation principles developed to guide the Hub’s participatory design. The fourth presents nine learner personas spanning junior, mid-career and senior researchers, a bespoke metadata taxonomy, and AI-assisted tagging workflows that enable personalised learning pathways. The fifth outlines the RCH CPD2 (Context, Philosophy and Delivery) model, which informs the evaluation framework and the Flexible Fund mechanism for commissioning community-driven research and training. The symposium contributes to CALRG’s long-standing interest in technology-enhanced learning by examining what happens when educational technology infrastructure operates at national scale and across sectors.

This symposium presents early insights from the Hub’s first operational year across five interconnected workstreams. The first presentation frames the RCH within ESRC strategy, positioning research capability as national infrastructure. The second reports on ecosystem curation: 934 resources catalogued across 15 sources and 12 thematic areas, revealing significant gaps in existing provision. The third examines co-creation, drawing on Q-methodology data, evidence cafes, and a set of ten co-creation principles developed to guide the Hub’s participatory design. The fourth presents nine learner personas spanning junior, mid-career and senior researchers, a bespoke metadata taxonomy, and AI-assisted tagging workflows that enable personalised learning pathways. The fifth outlines a macro–meso–micro evaluation framework and the Flexible Fund mechanism for commissioning community-driven research and training. The symposium contributes to CALRG’s long-standing interest in technology-enhanced learning by examining what happens when educational technology infrastructure operates at national scale and across sectors.

Better Words for AI – a collaborative dynamic lexicon

Mirjam Hauck, Eleanor Moore, Olivia Kelly, Julia Molinari

The Open University, UK

Abstract: 

Better Words for AI is a cross‑school research project funded by Praxis that aims to co‑create a dynamic, lexicon of Artificial Intelligence terminology collaboratively developed with students and staff from WELS. Motivated by the proliferation of ambiguous, anthropomorphic or hype‑driven language surrounding AI, the project addresses how unclear terminology can hinder understanding, exacerbate inequalities, and impede responsible use of AI in learning and teaching. Grounded in Critical AI Literacy (CAIL) and informed by a participatory approach, the project engages students and Associate Lecturers as co‑researchers throughout five phases, including focus groups, workshops, iterative peer review of lexicon entries and pilot deployment on OpenLearn Create. The resulting lexicon will offer ethically framed definitions, supporting clearer communication and enhancing pedagogical practice across disciplines. Beyond producing an open educational resource, the project aims to strengthen student voice, contribute to institutional EDIA priorities, and model a scalable approach to responsible AI communication at the OU and within the UK higher education sector.

Proposing Inclusive Ethical AI Policy Guidelines for Higher Education

Munir Moosa Sadruddin

World Institute on Disability

Abstract:

Persons with disabilities are particularly underrepresented in higher education, with only around 3% of adults accessing it (United Nations, 2020). Artificial Intelligence has emerged as a promising tool for making education more cost-effective, accessible, equitable, and engaging for learners with disabilities (OECD, 2024). The OHCHR (2021) report highlights the importance of technology for learners with disabilities in improving education, enhancing access to goods and services, and promoting inclusion and equality. Similarly, the UNESCO Recommendation on the Ethics of Artificial Intelligence emphasizes the need to promote inclusive AI (UNESCO, 2022).

This presentation proposes Inclusive Ethical AI Policy Guidelines for higher education institutions to ensure the visibility and representation of learners with disabilities in AI use. These guidelines create opportunities for collaboration, inclusion, and the development of innovative solutions for and by learners with disabilities, thereby supporting their educational experiences.

Re-visioning teaching practice and teacher professional development at scale through the lens of 360° video

Simon Cross

Institute of Educational Technology (IET), The Open University, UK

Abstract:

This summer, a project led by the OU will publish a paper setting out a vision for using 360° video at scale for teacher professional development (TPD) and training. In this presentation, I will describe the journey travelled over the last eighteen months to develop the vision, highlighting the teams’ original fieldwork into how teachers experimented with and used 360° video recordings and viewing to support their teaching practice and TPD and some new perspectives glimpsed by a reading 360° video from the perspective of spatial theory.

The focus of our fieldwork has been a joint project led by the OU and the National Institute of Applied Science in Bengaluru, India. In this, we have worked with over thirty-five teachers and teacher educators working in thirteen schools to co-create knowledge in authentic settings about how 360° video could be used support their teaching and professional development. We loaned each school an ‘equipment pack’ comprising a video camera, mobile device, VR headset, tripod and associated peripherals, and invited them to experiment and test uses for the technology however and wherever they wanted to. Training, on-site support and a community group were provided. Our loan cycles lasted weeks, giving teachers the opportunity to become familiar with and use the equipment without any preconditions.

Given that very little research has been conducted about the use of 360° video for in-service TPD, not only does this fieldwork offer new perspectives, it also underscores the need for further theorisation around the impact that 360° video technology has on how teachers envision their classroom. For this, our vision draws upon sociospatial theories of place, network and positionality. This adds some conceptual twists to a growing narrative that figures 360° video as a unique and valuable addition to teacher professional development programmes.

From Personalisation to Adaptation: Synthesising the Evidence and Extending the Framework

Maria Aristeidou, Felipe Tessarolo

The Open University, UK

Abstract:

Adaptive learning refers to the use of AI and data-driven technologies to dynamically adjust educational content, pacing, and feedback to individual learner needs. It has attracted growing institutional attention in higher education, yet significant conceptual confusion persists around what it is, what it does, and how it relates to personalised learning and differentiated instruction. In this presentation, we draw on a desk review carried out at the Institute of Educational Technology to clarify these distinctions and synthesise current evidence for institutional practice.

The first part of the presentation provides a conceptual overview of adaptive learning, distinguishing it from related approaches, tracing its development across three technological generations, and situating it within distance and asynchronous online education contexts.

The second part presents findings from an umbrella review of 13 rigorous systematic and scoping reviews published between 2015 and 2025, identified through a PRISMA-aligned search of Web of Science, Scopus, and ERIC. Using thematic meta-synthesis, we identified seven themes spanning: foundational concepts and architecture; technologies and digital infrastructure; the role of teachers and human agency; effectiveness and learning outcomes; ethical concerns and governance; student agency and learner autonomy; and learning pathways and curricular design. Academic performance improves in 59% of studies, with effect sizes ranging from small to large depending on context, discipline, and implementation quality. Larger effects are consistently found in informal learning and distance contexts, and learner-controlled progression outperforms purely system-controlled approaches for complex skill development. Insufficient teacher preparation and inadequate ethical governance emerge as the primary barriers to effective implementation.

The third part introduces an extended eight-dimensional framework for adaptive learning, building on FitzGerald et al.’s (2018) six-dimensional personalisation framework. The extension incorporates distance-learning contexts, dynamic learner modelling, a reconceptualised view of adaptive agency across technological, teacher, and learner dimensions, contemporary mechanisms, including generative AI, and two new dimensions addressing ethical governance and curricular design. The framework offers a practical analytical tool for institutions designing, evaluating, or scaling adaptive learning.

Teachers, Tools, and AI: Understanding the Role of Generative Systems in Curriculum Production

Thomas Ullmann, Duygu Bektik, Chris Edwards, Denise Whitelock, Christothea Herodotou

Institute of Educational Technology (IET), The Open University, UK

Abstract:

The rapid emergence and uptake of generative AI (genAI) is reshaping educational practice, with recent reports indicating that over half of teachers already use these systems for planning, assessment, communication, and other pedagogical tasks. Despite this widespread adoption, empirical research on how genAI can meaningfully support teaching remains limited. This presentation approaches genAI from an educational technology perspective and introduces findings from a multi phase investigation into how generative AI can aid curriculum production in higher education.

Our project examined the potential of genAI across a broad range of course writing activities, including developing learning outcomes, drafting outlines, designing activities and assessments, and reusing existing materials. To support this work, we developed Scribe, a collaborative AI enhanced workspace populated with customisable assistants such as quiz designers and activity sketchers. Scribe enables educators to inspect, adapt, and create their own AI assistants, upload materials for context-aware drafting, and collaborate within shared team spaces.

Our findings indicate that genAI can effectively support ideation, rapid drafting, and adherence to writing guidance, offering course teams a productive “third perspective” during early development stages. However, genAI remains an assistive rather than autonomous technology: outputs require expert evaluation for accuracy, bias, and pedagogical suitability. Key challenges include ensuring effective use of subject specific knowledge, avoiding teacher disempowerment by outsourcing creative tasks, and supporting educators in articulating tacit expertise through effective prompting.

Overall, our results suggest that genAI can enhance curriculum production when embedded within collaborative, reflective academic practice, with opportunities for increased productivity alongside important considerations for training, governance, and pedagogical quality.

Keynote: Reclaiming the Utopian Surplus: Open Education in an Age of Technological Fatigue

Federating Openness: The Global Open Graduate Network Pilot Hub Initiative 

Beck Pitt, Rob Farrow & Carina Bossu

Institute of Educational Technology (IET), The Open University, UK.

Abstract:

Since 2013, the Global Open Graduate Network (GO-GN) has supported doctoral and postdoctoral research in open education and practices worldwide. In 2023, we conducted a 10 year anniversary strategic review (Farrow et al., 2024) with our membership and the wider GO-GN and open education communities. This review captured the network’s achievements to date and future aspirations, including exploration of a more federated approach for the network.

This presentation reports on the outcome of this work, which focused on a pilot programme to establish and evaluate four regional hubs (Asia-Pacific, Canada, Ibero-America and Kenya). We will report on the development of these regional hubs across six continents, relating insights from the evaluation and reflecting on how other open education networks might approach questions of scale, diversity and sustainability.

Embedding Co-Design and Citizen Science Practices with Diverse Youth: Case Studies from the PEACE of Mind Project

Natalie Divin, Jessica Carr, Jay Martin, Christothea Herodotou

Institute of Educational Technology (IET), The Open University, UK & Inspire Wellbeing, UK.

Abstract:

The PEACE of Mind project is a cross-border, multi-partner project aimed at addressing high rates of poor youth wellbeing across Northern Ireland and the border counties (Bunting et al., 2020; Lynch et al., 2022) and contributing to peace and prosperity in the area. Project partners work to achieve this aim via a multi-modal delivery model, delivering wellbeing interventions to a range of young people within mainstream schools, special schools and community groups to maximise inclusivity. Project partners deliver a six-week tailored programme using creative methods to improve mental wellbeing and build resilience in young people aged between 9-25 years. Participants can subsequently train to become peer mentors, helping to embed the programme further within their local communities.

This presentation will share case studies on the steps taken and resources used by the research team to minimise barriers to co-creation within the PEACE of Mind project. These case studies include working with young people with learning disabilities to minimise acquiescence, minimising power imbalances when gaining authentic feedback from young people, and empowering youth to design citizen science studies using the nQuire platform that directly relate to their experience of being a young person within Northern Ireland and the border counties.